Renewable Energy Dataset

library(readr)
energy <- read_csv('../../data/IRENA data.csv', skip=1)
## Rows: 67200 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Country/area, Technology, Data Type, Grid connection, Electricity s...
## dbl (1): Year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Gapminder Dataset

library(gapminder)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
data(gapminder)

Carbon Emissions

library(readr)
library(dplyr)
url <- 'https://nyc3.digitaloceanspaces.com/owid-public/data/co2/owid-co2-data.csv'
carbon <- read_csv(url)
## Rows: 50191 Columns: 79
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): country, iso_code
## dbl (77): year, population, gdp, cement_co2, cement_co2_per_capita, co2, co2...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Energy Economics

url <- 'https://raw.githubusercontent.com/ericmkeen/sewanee_esus/master/02_energy_sector/levelized-cost-of-energy.csv'
econ <- read_csv(url)
## Rows: 3402 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, source
## dbl (2): year, cost
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Onshore Wind Energy Big 3

ggplotly(big3)

According to the World Wind Energy Association, China, Germany, and the United States act as the world’s ‘Onshore Wind Energy Big 3’ as it relates to their respective installed wind energy capacity. The chart above demonstrates each of the Big 3’s relative levelized cost per kilowatt-hour (kWh) of onshore wind energy (in 2024 USD) throughout the years. Germany and the United States were the first to implement onshore wind energy at a large scale in the mid-80s. At first, Germany’s onshore wind energy was much more affordable (i.e., $0.25 in 1984) than the United States’ (i.e., $0.32 in 1984). By the time China joined the game in the late 90s, each of the countries were pretty much neck and neck. Fast-forward to modern day (i.e., 2021), China just barely leads the way in affordability at $0.028 per kilowatt-hour, the United States falls in second at $0.029, and Germany drags behind at $0.051. Data source found here.

Monetary Value of Solar Energy Produced in Japan (2011-2021)

ggplotly(japan)

Solar PV is a top economic driver in Japan’s energy sector. The graph above demonstrates the yearly monetary value produced from solar energy in Japan within a single decade. In just 10 years, the value exceeds $65 billion. The lowest value produced in a year (i.e., 2012) was $2.07 billion (i.e., $17 for each Japanese citizen). Data source found here and here.

Difference Between Ireland and Africa’s Methane Levels (1900-2000)

ggplotly(rica)

Methane is one the most dangerous greenhouse gases (GHGs) to be released into the atmosphere because it is extremely effective at trapping heat. Africa is experiencing accelerated global warming compared to other continents according to the World Meteorological Organization. An interesting visualization published by Veronika Samborska on Our World in Data has revealed that the country of Ireland has experienced a declining global warming rate since the 1940s. The visualization above illustrates the difference in yearly methane levels between Africa and Ireland that could be potentially contributing to this vastly different global warming rate. As depicted by the graph, Ireland’s methane levels were just above 200,000 metric tons in 2000, whereas Africa’s reached approximately 1.25 billion metric tons. Data source found here. ## Relationship between GDP per Capita and CO2 per Capita

ggplotly(co2gdp)

I was curious if GDP per capita and CO2 per capita were directly correlated. I selected a handful of different countries with widely distributed GDPs and compared. Although there appear to be a positive correlation trend in some countries (i.e., Spain, Argentine, Japn, and Mexico), other countries seem to stagnate. Data sources include this and r’s stock ‘gapminder’.